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Background: This in silico study investigated the association between the local biosynthesis of cholesterol and mammographic density, the major risk of developing breast cancer, as a function of the three cellular components of breast tissue (epithelium, fatty, and non-fatty stroma). Methods: The study compared the expression of 7 genes (HMGCR, FDPS, FDFT1, GGPS1, SQLE, LSS, and SREBF2) involved in the de novo cholesterol biosynthesis, first, according to the radiological density (dense vs. non-dense breast) and, then, according to the cellular components of breast tissue, regardless of the radiological classification. Results: HMGCR, SQLE, and SBREF2 were significantly more frequently expressed in radiologically dense than in non-dense breasts (-1.70 vs. -1.41, P=0.0028; -1.20 vs. -1.11, P=0.0501; -3.63 vs. -3.31 P=0.0003; -0.92 vs. -0.76, P=0.0271, respectively). When the samples were reclassified based on their cellular components as highly fatty and highly non-fatty, HMGCR, SQLE, and SBREF2 were significantly more frequently expressed in highly non-fatty samples (-1.48 vs. -1.94, P<0.0001; -3.39 vs. -4.18, P<0.0001; -0.77 vs. -0.94, P=0.0103, respectively), whereas LSS was overexpressed in high fatty ones (0.28 vs. -0.60, P<0.0001). Besides, while in the highly non-fatty subgroup, SREBF2 was positively associated with both HMGCR (r=0.53, P<0.0001) and SQLE (r=0.73, P<0.0001), in the highly fatty subgroup, these positive correlations disappeared (SREBF2*HMGCR: r=-0.19, P=0.3026) or substantially decreased (SREBF2*SQLE: r=0.41, P=0.0173). Conclusion: Findings provide a compelling biological explanation for the clinical evidence that women with radiologically dense breasts are at a higher risk of developing cancer compared to those with non-dense breasts because of the prevalence of non-fatty tissue, where the altered expression of genes leading to an increased cholesterol production can contribute to the transformation of epithelial cells, and support the use of mammographic density as a reliable surrogate marker to identify women who may benefit from a preventive treatment aimed at reducing cholesterol production. [ABSTRACT FROM AUTHOR]

Abstract The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy.

Abstract Background the study was designed to determine how tumour hormone receptor status affects the subsequent pattern over time (dynamics) of breast cancer recurrence and death following conservative primary breast cancer resection. Methods Time span from primary resection until both first recurrence and death were considered among 2825 patients undergoing conservative surgery with or without breast radiotherapy. The hazard rates for ipsilateral breast tumour recurrence (IBTR), distant metastasis (DM) and mortality throughout 10 years of follow-up were assessed. Results DM dynamics displays the same bimodal pattern (first early peak at about 24 months, second late peak at the sixth-seventh year) for both estrogen receptor (ER) positive (P) and negative (N) tumours and for all local treatments and metastatic sites. The hazard rates for IBTR maintain the bimodal pattern for ERP and ERN tumours; however, each IBTR recurrence peak for ERP tumours is delayed in comparison to the corresponding timing of recurrence peaks for ERN tumours. Mortality dynamics is markedly different for ERP and ERN tumours with more early deaths among patients with ERN than among patients with ERP primary tumours. Conclusion DM dynamics is not influenced by the extent of conservative primary tumour resection and is similar for both ER phenotypes across different metastatic sites, suggesting similar mechanisms for tumour development at distant sites despite apparently different microenvironments. The IBTR risk peak delay observed in ERP tumours is an exception to the common recurrence risk rhythm. This suggests that the microenvironment within the residual breast tissue may enforce more stringent constraints upon ERP breast tumour cell growth than other tissues, prolonging the latency of IBTR. This local environment is, however, apparently less constraining to ERN cells, as IBTR dynamics is similar to the corresponding recurrence dynamics among other distant tissues.

Summary: Epidemiological studies demonstrated that, in postmenopausal women, high circulating levels of testosterone, especially when associated with weight gain, positively correlated with an increased risk of breast cancer because of the augmented production of oestrogen via testosterone aromatization in the adipose tissue. Besides, growing evidence suggests that sulfatase can increase the tissue concentration of bioactive estradiol through the reconversion of estrone sulfate, thus providing a favourable milieu for epithelial cells expressing the oestrogen receptor. In this review, we will discuss how the "obesity‐insulin‐testosterone" connection and the abnormal production of bioactive oestrogen ‐ as a result of the conversion of the androgens by aromatase and the estrone reconversion by sulfatase−, may affect the response to hormone therapy and the outcome of postmenopausal breast cancer patients, and how a combined therapy including metformin, anti‐inflammatory drugs, and aromatase/sulfatase inhibitors could successfully improve patient's outcome. [ABSTRACT FROM AUTHOR]